.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_example_1_local_sequential.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_example_1_local_sequential.py:


Example 1 - Local and Sequential
================================




.. code-block:: python

    import logging
    logging.basicConfig(level=logging.WARNING)

    import argparse

    import hpbandster.core.nameserver as hpns
    import hpbandster.core.result as hpres

    from hpbandster.optimizers import BOHB as BOHB
    from hpbandster.examples.commons import MyWorker



    parser = argparse.ArgumentParser(description='Example 1 - sequential and local execution.')
    parser.add_argument('--min_budget',   type=float, help='Minimum budget used during the optimization.',    default=9)
    parser.add_argument('--max_budget',   type=float, help='Maximum budget used during the optimization.',    default=243)
    parser.add_argument('--n_iterations', type=int,   help='Number of iterations performed by the optimizer', default=4)
    args=parser.parse_args()


    # Step 1: Start a nameserver
    # Every run needs a nameserver. It could be a 'static' server with a
    # permanent address, but here it will be started for the local machine with the default port.
    # The nameserver manages the concurrent running workers across all possible threads or clusternodes.
    # Note the run_id argument. This uniquely identifies a run of any HpBandSter optimizer.
    NS = hpns.NameServer(run_id='example1', host='127.0.0.1', port=None)
    NS.start()

    # Step 2: Start a worker
    # Now we can instantiate a worker, providing the mandatory information
    # Besides the sleep_interval, we need to define the nameserver information and
    # the same run_id as above. After that, we can start the worker in the background,
    # where it will wait for incoming configurations to evaluate.
    w = MyWorker(sleep_interval = 0, nameserver='127.0.0.1',run_id='example1')
    w.run(background=True)

    # Step 3: Run an optimizer
    # Now we can create an optimizer object and start the run.
    # Here, we run BOHB, but that is not essential.
    # The run method will return the `Result` that contains all runs performed.
    bohb = BOHB(  configspace = w.get_configspace(),
                  run_id = 'example1', nameserver='127.0.0.1',
                  min_budget=args.min_budget, max_budget=args.max_budget
               )
    res = bohb.run(n_iterations=args.n_iterations)

    # Step 4: Shutdown
    # After the optimizer run, we must shutdown the master and the nameserver.
    bohb.shutdown(shutdown_workers=True)
    NS.shutdown()

    # Step 5: Analysis
    # Each optimizer returns a hpbandster.core.result.Result object.
    # It holds informations about the optimization run like the incumbent (=best) configuration.
    # For further details about the Result object, see its documentation.
    # Here we simply print out the best config and some statistics about the performed runs.
    id2config = res.get_id2config_mapping()
    incumbent = res.get_incumbent_id()

    print('Best found configuration:', id2config[incumbent]['config'])
    print('A total of %i unique configurations where sampled.' % len(id2config.keys()))
    print('A total of %i runs where executed.' % len(res.get_all_runs()))
    print('Total budget corresponds to %.1f full function evaluations.'%(sum([r.budget for r in res.get_all_runs()])/args.max_budget))

**Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_example_1_local_sequential.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: example_1_local_sequential.py <example_1_local_sequential.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: example_1_local_sequential.ipynb <example_1_local_sequential.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_